{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0df8321-fb41-4061-b520-46c504cbbffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import glob, os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5be2e8f1-c92f-489e-aa7c-92f078e39c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_mat = glob.glob('./旋转机械故障诊断挑战赛公开数据/训练集/*/*/*.mat')\n",
    "train_mat.sort()\n",
    "\n",
    "train_wav = glob.glob('./旋转机械故障诊断挑战赛公开数据/训练集/*/*/*.wav')\n",
    "train_wav.sort()\n",
    "\n",
    "val_mat = glob.glob('./旋转机械故障诊断挑战赛公开数据/验证集/*/*/*.mat')\n",
    "val_mat.sort()\n",
    "\n",
    "val_wav = glob.glob('./旋转机械故障诊断挑战赛公开数据/验证集/*/*/*.wav')\n",
    "val_wav.sort()\n",
    "\n",
    "test_mat = glob.glob('./旋转机械故障诊断挑战赛公开数据/测试集/*.mat')\n",
    "test_mat.sort()\n",
    "\n",
    "test_wav = glob.glob('./旋转机械故障诊断挑战赛公开数据/测试集/*.wav')\n",
    "test_wav.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "908b957c-639a-4eb6-820c-9cbfb1e65ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_mat_feat = [scipy.io.loadmat(path)['vib_data'].reshape(-1) for path in train_mat]\n",
    "valid_mat_feat = [scipy.io.loadmat(path)['vib_data'].reshape(-1) for path in val_mat]\n",
    "test_mat_feat = [scipy.io.loadmat(path)['vib_data'].reshape(-1) for path in test_mat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "55579105-dac4-4a20-a518-a0593303f6e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label = [path.split('_')[-4] for path in train_mat]\n",
    "val_label = [path.split('_')[-4] for path in val_mat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ce851a7-d42d-4534-81d9-7d988a2a5c54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c94e1eaa-88cb-4d2a-8df7-ecfcadfe9fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsClassifier()\n",
    "model.fit(train_mat_feat, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "86215c73-2310-40e2-868d-1ae4bd464758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(valid_mat_feat, val_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "641713d7-296a-4c95-91f0-3acf63abfac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['normal', 'cage', 'normal', 'cage', 'cage', 'cage', 'cage',\n",
       "       'normal', 'normal', 'cage', 'cage', 'cage', 'normal', 'normal',\n",
       "       'normal', 'normal', 'cage', 'normal', 'normal', 'cage', 'normal',\n",
       "       'normal', 'normal', 'normal', 'normal', 'normal', 'normal', 'cage',\n",
       "       'normal', 'cage', 'normal', 'normal', 'cage', 'cage', 'normal',\n",
       "       'normal', 'normal', 'normal', 'cage', 'normal', 'cage', 'normal',\n",
       "       'normal', 'normal', 'normal', 'cage', 'cage', 'normal', 'cage',\n",
       "       'cage', 'cage', 'cage', 'normal', 'normal', 'normal', 'cage',\n",
       "       'normal', 'normal', 'cage', 'normal', 'normal', 'normal', 'normal',\n",
       "       'normal', 'cage', 'normal', 'cage', 'normal', 'cage', 'normal',\n",
       "       'cage', 'normal', 'normal', 'normal', 'normal', 'normal', 'normal',\n",
       "       'normal', 'normal', 'cage', 'normal', 'normal', 'normal', 'normal',\n",
       "       'normal', 'normal', 'cage', 'cage', 'normal', 'normal', 'cage',\n",
       "       'normal', 'cage', 'normal', 'cage', 'normal', 'normal', 'normal',\n",
       "       'normal', 'cage', 'normal', 'cage', 'normal', 'cage', 'cage',\n",
       "       'cage', 'cage', 'cage', 'normal', 'cage', 'cage', 'normal',\n",
       "       'normal', 'normal', 'normal', 'cage', 'normal', 'normal', 'normal',\n",
       "       'cage', 'cage', 'normal', 'cage', 'normal', 'cage', 'cage',\n",
       "       'normal', 'cage', 'normal', 'normal', 'normal', 'normal', 'cage',\n",
       "       'normal', 'normal', 'normal', 'normal', 'cage', 'cage', 'normal',\n",
       "       'cage', 'cage', 'normal', 'normal', 'normal', 'normal', 'normal',\n",
       "       'normal', 'normal', 'cage', 'normal', 'roller', 'normal', 'normal',\n",
       "       'cage', 'normal', 'normal', 'normal', 'normal', 'normal', 'normal',\n",
       "       'normal', 'normal', 'normal', 'normal', 'normal', 'cage', 'cage',\n",
       "       'normal', 'normal', 'cage', 'cage', 'normal', 'normal', 'normal',\n",
       "       'normal', 'cage', 'normal', 'normal', 'normal', 'normal', 'normal',\n",
       "       'normal', 'normal', 'normal', 'normal', 'cage', 'cage', 'cage',\n",
       "       'normal', 'normal', 'cage', 'normal', 'cage', 'normal', 'cage',\n",
       "       'cage', 'normal', 'normal', 'cage', 'normal', 'normal', 'roller',\n",
       "       'normal', 'normal', 'normal', 'normal', 'normal', 'normal', 'cage',\n",
       "       'normal', 'normal', 'normal', 'cage', 'normal', 'cage', 'roller',\n",
       "       'normal', 'cage', 'normal', 'cage', 'normal', 'cage', 'normal',\n",
       "       'normal', 'cage', 'normal', 'cage', 'normal', 'normal', 'cage',\n",
       "       'cage', 'cage', 'cage', 'normal', 'normal', 'cage', 'normal',\n",
       "       'normal', 'normal', 'cage', 'cage', 'normal', 'normal', 'normal',\n",
       "       'normal', 'normal', 'normal', 'normal', 'cage', 'normal', 'cage',\n",
       "       'cage', 'normal', 'cage', 'normal', 'normal', 'cage', 'cage',\n",
       "       'normal', 'cage', 'cage', 'cage', 'cage', 'normal', 'normal',\n",
       "       'normal', 'normal', 'cage', 'cage', 'normal', 'normal', 'normal',\n",
       "       'normal', 'normal', 'normal', 'normal', 'normal', 'normal',\n",
       "       'normal', 'normal', 'normal', 'normal', 'cage', 'normal', 'normal',\n",
       "       'cage', 'normal', 'normal', 'normal', 'normal', 'normal', 'cage',\n",
       "       'normal', 'cage', 'normal', 'normal', 'normal', 'normal', 'normal',\n",
       "       'normal', 'cage', 'normal', 'normal', 'cage', 'normal', 'normal',\n",
       "       'normal', 'cage', 'normal', 'normal', 'cage', 'normal', 'cage',\n",
       "       'cage', 'cage', 'cage', 'normal', 'normal', 'normal'], dtype='<U6')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a339a6f8-1e5b-4a03-aa4a-72960f089799",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        'audio_name': [x.split('/')[-1] for x in test_wav],\n",
    "        'label': model.predict(test_mat_feat)\n",
    "    }\n",
    ").to_csv('submit.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb951b6a-abc3-45da-ba64-0b890c48be7e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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